MCP CNPJ Intelligence

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the similarity algorithm (CNAE, size, region), which adds valuable behavioral context beyond basic functionality. However, it doesn't cover aspects like performance characteristics, error handling, or output format, leaving gaps in behavioral understanding for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, usage guidelines, algorithm), front-loaded with the core purpose. Every sentence adds value: the first states what it does, the second provides usage scenarios, and the third explains the similarity criteria. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is moderately complete. It covers purpose, usage, and algorithm well, but lacks details on output format, error cases, or performance limits. For a tool with 4 parameters and complex similarity matching, more behavioral context would be beneficial to fully guide the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining the multi-dimensional scoring approach (CNAE, capital, region), which helps interpret the 'score_minimo' parameter's significance. This elevates the score above baseline, though it doesn't provide detailed syntax beyond what the schema offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Encontra') and resource ('empresas SIMILARES'), distinguishing it from siblings like 'buscar_empresa' (find specific company) or 'buscar_por_cnae' (find by activity code). It specifies 'scoring multi-dimensional' for similarity matching, making the purpose distinct and well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'QUANDO USAR' section explicitly provides two scenarios for when to use this tool: for lookalike analysis of good clients and for portfolio expansion with similar companies. This gives clear, actionable guidance that differentiates it from alternatives like 'benchmark_empresa' or 'buscar_avancado' without needing to list exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.